Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To
attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability
of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based
on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results
show that the proposed method is effective

In our study we use a kernel based classification
technique, Support Vector Machine Regression for predicting the
Melting Point of Drug – like compounds in terms of Topological
Descriptors, Topological Charge Indices, Connectivity Indices
and 2D Auto Correlations. The Machine Learning model was
designed, trained and tested using a dataset of 100 compounds
and it was found that an SVMReg model with RBF Kernel could
predict the Melting Point with a mean absolute error 15.5854 and
Root Mean Squared Error 19.7576

Multispectral analysis is a promising approach in
tissue classification and abnormality detection from Magnetic
Resonance (MR) images. But instability in accuracy and
reproducibility of the classification results from conventional
techniques keeps it far from clinical applications. Recent studies
proposed Independent Component Analysis (ICA) as an effective
method for source signals separation from multispectral MR data.
However, it often fails to extract the local features like small
abnormalities, especially from dependent real data. A multisignal
wavelet analysis prior to ICA is proposed in this work to resolve
these issues. Best de-correlated detail coefficients are combined
with input images to give better classification results.
Performance improvement of the proposed method over
conventional ICA is effectively demonstrated by segmentation
and classification using k-means clustering. Experimental results
from synthetic and real data strongly confirm the positive effect
of the new method with an improved Tanimoto index/Sensitivity
values, 0.884/93.605, for reproduced small white matter lesions

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International conference on Communication and Signal Processing, April 3-5, 2013, India